AI-ENABLED INTERVIEW ANALYSIS: UNVEILING INSIGHTS AND ENHANCING DECISION-MAKING IN HUMAN RESOURCE MANAGEMENT
Vikas1,a), Tanuja Tomer2,b), Rajneesh Panwar3,c)
1,2,3Assistant Professor
1,2,3,School of Computer Science and Applications, IIMT University, Meerut, U.P, INDIA
a),b)Corresponding author: vicky.c610@gmail.com , tanujatomer88@gmail.com
c)Another author: rajpanwar0710@gmail.com
Abstract:
The process of conducting interviews plays a pivotal role in human resource management, as it directly influences the selection of candidates and ultimately shapes the composition of an organization's workforce. However, traditional interview methods are often subjective and prone to biases, leading to suboptimal decision-making. In recent years, the emergence of artificial intelligence (AI) technologies has provided new opportunities to revolutionize interview analysis and improve decision-making in the hiring process.This research paper explores the application of AI-enabled interview analysis in human resource management, aiming to unveil insights and enhance decision-making.
The study focuses on three main areas: interview data collection, analysis, and decision-making support. AI technologies, including natural language processing (NLP) and machine learning algorithms, are leveraged to analyze interview transcripts, audio recordings, and other relevant data sources.By analyzing interview data using AI, this research uncovers hidden patterns, linguistic cues, and behavioural indicators that may be missed by human interviewers. These insights provide a deeper understanding of candidate suitability, skills, and competencies, enabling HR professionals to make more informed and objective decisions. Moreover, AI algorithms can be trained to identify biases and mitigate their impact, leading to fairer and more inclusive hiring practices. The research also investigates the potential challenges and ethical considerations associated with AI-enabled interview analysis. Concerns related to privacy, data security, algorithmic biases, and the role of human judgment in decision-making are examined, highlighting the need for responsible implementation and ongoing monitoring.To validate the effectiveness of AI-enabled interview analysis, a mixed-methods approach is employed, combining quantitative data analysis and qualitative feedback from HR professionals. The study presents empirical evidence demonstrating the benefits of AI technologies in improving decision-making accuracy, efficiency, and fairness.
Key Index Terms:
Digital AI-enabled interview analysis, Hiring process, Decision-making, Interview data analysis, Natural language processing (NLP), Machine learning algorithms, Candidate selection, Workforce composition, Objective decision-making, Hidden patterns, Linguistic cues.
